From 0a5a4f0d83c6a472487e13a29782098f2bb8dd15 Mon Sep 17 00:00:00 2001
From: Nat Jeffries <njeff@google.com>
Date: Mon, 4 May 2020 15:13:42 -0700
Subject: [PATCH] Use persistent buffer in conv and xtensa_hifimini/conv.
PiperOrigin-RevId: 309825806
Change-Id: Ia5266ae0b0902ee3dc64f96955a76838ad96d45a
---
.../image_recognition_test.cc | 2 +-
.../image_recognition_experimental/main.cc | 2 +-
.../person_detection/main_functions.cc | 2 +-
.../person_detection/person_detection_test.cc | 2 +-
.../main_functions.cc | 2 +-
.../person_detection_test.cc | 2 +-
tensorflow/lite/micro/kernels/conv.cc | 157 +++++++++++-------
.../micro/kernels/xtensa_hifimini/conv.cc | 62 ++++---
8 files changed, 135 insertions(+), 96 deletions(-)
diff --git a/tensorflow/lite/micro/examples/image_recognition_experimental/image_recognition_test.cc b/tensorflow/lite/micro/examples/image_recognition_experimental/image_recognition_test.cc
index d4dfee41a8b..fd547b433ef 100644
--- a/tensorflow/lite/micro/examples/image_recognition_experimental/image_recognition_test.cc
+++ b/tensorflow/lite/micro/examples/image_recognition_experimental/image_recognition_test.cc
@@ -53,7 +53,7 @@ TF_LITE_MICRO_TEST(TestImageRecognitionInvoke) {
micro_op_resolver.AddBuiltin(tflite::BuiltinOperator_SOFTMAX,
tflite::ops::micro::Register_SOFTMAX());
- const int tensor_arena_size = 45 * 1024;
+ const int tensor_arena_size = 50 * 1024;
uint8_t tensor_arena[tensor_arena_size];
tflite::MicroInterpreter interpreter(model, micro_op_resolver, tensor_arena,
diff --git a/tensorflow/lite/micro/examples/image_recognition_experimental/main.cc b/tensorflow/lite/micro/examples/image_recognition_experimental/main.cc
index 613c97f15be..09c76df0379 100644
--- a/tensorflow/lite/micro/examples/image_recognition_experimental/main.cc
+++ b/tensorflow/lite/micro/examples/image_recognition_experimental/main.cc
@@ -67,7 +67,7 @@ int main(int argc, char** argv) {
micro_op_resolver.AddBuiltin(tflite::BuiltinOperator_SOFTMAX,
tflite::ops::micro::Register_SOFTMAX());
- constexpr int tensor_arena_size = 45 * 1024;
+ constexpr int tensor_arena_size = 50 * 1024;
uint8_t tensor_arena[tensor_arena_size];
tflite::MicroInterpreter interpreter(model, resolver, tensor_arena,
tensor_arena_size, error_reporter);
diff --git a/tensorflow/lite/micro/examples/person_detection/main_functions.cc b/tensorflow/lite/micro/examples/person_detection/main_functions.cc
index 279b09e4c2b..0e5c6394d56 100644
--- a/tensorflow/lite/micro/examples/person_detection/main_functions.cc
+++ b/tensorflow/lite/micro/examples/person_detection/main_functions.cc
@@ -34,7 +34,7 @@ tflite::MicroInterpreter* interpreter = nullptr;
TfLiteTensor* input = nullptr;
// An area of memory to use for input, output, and intermediate arrays.
-constexpr int kTensorArenaSize = 73 * 1024;
+constexpr int kTensorArenaSize = 93 * 1024;
static uint8_t tensor_arena[kTensorArenaSize];
} // namespace
diff --git a/tensorflow/lite/micro/examples/person_detection/person_detection_test.cc b/tensorflow/lite/micro/examples/person_detection/person_detection_test.cc
index 51a61881ead..8acb93ced17 100644
--- a/tensorflow/lite/micro/examples/person_detection/person_detection_test.cc
+++ b/tensorflow/lite/micro/examples/person_detection/person_detection_test.cc
@@ -27,7 +27,7 @@ limitations under the License.
#include "tensorflow/lite/version.h"
// Create an area of memory to use for input, output, and intermediate arrays.
-constexpr int tensor_arena_size = 73 * 1024;
+constexpr int tensor_arena_size = 93 * 1024;
uint8_t tensor_arena[tensor_arena_size];
TF_LITE_MICRO_TESTS_BEGIN
diff --git a/tensorflow/lite/micro/examples/person_detection_experimental/main_functions.cc b/tensorflow/lite/micro/examples/person_detection_experimental/main_functions.cc
index 719f16b2d36..92d2c091f55 100644
--- a/tensorflow/lite/micro/examples/person_detection_experimental/main_functions.cc
+++ b/tensorflow/lite/micro/examples/person_detection_experimental/main_functions.cc
@@ -41,7 +41,7 @@ TfLiteTensor* input = nullptr;
// signed value.
// An area of memory to use for input, output, and intermediate arrays.
-constexpr int kTensorArenaSize = 125 * 1024;
+constexpr int kTensorArenaSize = 136 * 1024;
static uint8_t tensor_arena[kTensorArenaSize];
} // namespace
diff --git a/tensorflow/lite/micro/examples/person_detection_experimental/person_detection_test.cc b/tensorflow/lite/micro/examples/person_detection_experimental/person_detection_test.cc
index b0979735d4f..c3719e559ca 100644
--- a/tensorflow/lite/micro/examples/person_detection_experimental/person_detection_test.cc
+++ b/tensorflow/lite/micro/examples/person_detection_experimental/person_detection_test.cc
@@ -27,7 +27,7 @@ limitations under the License.
#include "tensorflow/lite/version.h"
// Create an area of memory to use for input, output, and intermediate arrays.
-constexpr int tensor_arena_size = 125 * 1024;
+constexpr int tensor_arena_size = 136 * 1024;
uint8_t tensor_arena[tensor_arena_size];
TF_LITE_MICRO_TESTS_BEGIN
diff --git a/tensorflow/lite/micro/kernels/conv.cc b/tensorflow/lite/micro/kernels/conv.cc
index 7334cf13e49..ff425e90ee8 100644
--- a/tensorflow/lite/micro/kernels/conv.cc
+++ b/tensorflow/lite/micro/kernels/conv.cc
@@ -33,7 +33,6 @@ constexpr int kInputTensor = 0;
constexpr int kFilterTensor = 1;
constexpr int kBiasTensor = 2;
constexpr int kOutputTensor = 0;
-constexpr int kMaxChannels = 1024;
// Conv is quantized along dimension 0:
// https://www.tensorflow.org/lite/performance/quantization_spec
@@ -49,9 +48,8 @@ struct OpData {
int output_shift;
// Per channel output multiplier and shift.
- // TODO(b/141139247): Allocate these dynamically when possible.
- int32_t per_channel_output_multiplier[kMaxChannels];
- int32_t per_channel_output_shift[kMaxChannels];
+ int32_t* per_channel_output_multiplier;
+ int32_t* per_channel_output_shift;
// The range of the fused activation layer. For example for kNone and
// uint8_t these would be 0 and 255.
@@ -72,10 +70,10 @@ inline PaddingType RuntimePaddingType(TfLitePadding padding) {
}
TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node,
- TfLiteConvParams* params, int width, int height,
- int filter_width, int filter_height, int out_width,
- int out_height, const TfLiteType data_type,
- OpData* data) {
+ const TfLiteConvParams* params, int width,
+ int height, int filter_width, int filter_height,
+ int out_width, int out_height,
+ const TfLiteType data_type, OpData* data) {
bool has_bias = node->inputs->size == 3;
// Check number of inputs/outputs
TF_LITE_ENSURE(context, has_bias || node->inputs->size == 2);
@@ -109,8 +107,69 @@ TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node,
return kTfLiteOk;
}
+void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+ TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
+ void* data = nullptr;
+ if (context->AllocatePersistentBuffer(context, sizeof(OpData), &data) ==
+ kTfLiteError) {
+ return nullptr;
+ }
+ return data;
+}
+
+TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+ TFLITE_DCHECK(node->user_data != nullptr);
+ TFLITE_DCHECK(node->builtin_data != nullptr);
+
+ OpData* data = static_cast<OpData*>(node->user_data);
+ const auto params = static_cast<const TfLiteConvParams*>(node->builtin_data);
+
+ TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+ const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+ const TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
+
+ int input_width = input->dims->data[2];
+ int input_height = input->dims->data[1];
+ int filter_width = filter->dims->data[2];
+ int filter_height = filter->dims->data[1];
+ int output_width = output->dims->data[2];
+ int output_height = output->dims->data[1];
+
+ // Dynimically allocate per-channel quantization parameters.
+ const int num_channels = filter->dims->data[kConvQuantizedDimension];
+ TF_LITE_ENSURE_STATUS(context->AllocatePersistentBuffer(
+ context, num_channels * sizeof(int32_t),
+ reinterpret_cast<void**>(&data->per_channel_output_multiplier)));
+ TF_LITE_ENSURE_STATUS(context->AllocatePersistentBuffer(
+ context, num_channels * sizeof(int32_t),
+ reinterpret_cast<void**>(&data->per_channel_output_shift)));
+
+ // All per-channel quantized tensors need valid zero point and scale arrays.
+ if (input->type == kTfLiteInt8) {
+ TF_LITE_ENSURE_EQ(context, filter->quantization.type,
+ kTfLiteAffineQuantization);
+
+ const auto* affine_quantization =
+ static_cast<TfLiteAffineQuantization*>(filter->quantization.params);
+ TF_LITE_ENSURE(context, affine_quantization);
+ TF_LITE_ENSURE(context, affine_quantization->scale);
+ TF_LITE_ENSURE(context, affine_quantization->zero_point);
+
+ TF_LITE_ENSURE(context,
+ affine_quantization->scale->size == 1 ||
+ affine_quantization->scale->size ==
+ filter->dims->data[kConvQuantizedDimension]);
+ TF_LITE_ENSURE_EQ(context, affine_quantization->scale->size,
+ affine_quantization->zero_point->size);
+ }
+
+ return CalculateOpData(context, node, params, input_width, input_height,
+ filter_width, filter_height, output_width,
+ output_height, input->type, data);
+} // namespace conv
+
void EvalQuantized(TfLiteContext* context, TfLiteNode* node,
- TfLiteConvParams* params, OpData* data,
+ TfLiteConvParams* params, const OpData& data,
const TfLiteTensor* input, const TfLiteTensor* filter,
const TfLiteTensor* bias, TfLiteTensor* im2col,
TfLiteTensor* hwcn_weights, TfLiteTensor* output) {
@@ -118,10 +177,11 @@ void EvalQuantized(TfLiteContext* context, TfLiteNode* node,
const int32_t filter_offset = -filter->params.zero_point;
const int32_t output_offset = output->params.zero_point;
+ // TODO(b/154032858): Investigate removing extra copies.
ConvParams op_params;
op_params.padding_type = RuntimePaddingType(params->padding);
- op_params.padding_values.width = data->padding.width;
- op_params.padding_values.height = data->padding.height;
+ op_params.padding_values.width = data.padding.width;
+ op_params.padding_values.height = data.padding.height;
op_params.stride_width = params->stride_width;
op_params.stride_height = params->stride_height;
op_params.dilation_width_factor = params->dilation_width_factor;
@@ -129,10 +189,10 @@ void EvalQuantized(TfLiteContext* context, TfLiteNode* node,
op_params.input_offset = input_offset;
op_params.weights_offset = filter_offset;
op_params.output_offset = output_offset;
- op_params.output_multiplier = data->output_multiplier;
- op_params.output_shift = -data->output_shift;
- op_params.quantized_activation_min = data->output_activation_min;
- op_params.quantized_activation_max = data->output_activation_max;
+ op_params.output_multiplier = data.output_multiplier;
+ op_params.output_shift = -data.output_shift;
+ op_params.quantized_activation_min = data.output_activation_min;
+ op_params.quantized_activation_max = data.output_activation_max;
reference_ops::Conv(op_params, GetTensorShape(input),
GetTensorData<uint8_t>(input), GetTensorShape(filter),
GetTensorData<uint8_t>(filter), GetTensorShape(bias),
@@ -142,11 +202,12 @@ void EvalQuantized(TfLiteContext* context, TfLiteNode* node,
}
void EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node,
- TfLiteConvParams* params, OpData* data,
+ TfLiteConvParams* params, const OpData& data,
const TfLiteTensor* input,
const TfLiteTensor* filter,
const TfLiteTensor* bias, TfLiteTensor* output,
TfLiteTensor* im2col) {
+ // TODO(b/154032858): Investigate removing extra copies.
ConvParams op_params;
op_params.input_offset = -input->params.zero_point;
op_params.output_offset = output->params.zero_point;
@@ -154,14 +215,14 @@ void EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node,
op_params.stride_width = params->stride_width;
op_params.dilation_height_factor = params->dilation_height_factor;
op_params.dilation_width_factor = params->dilation_width_factor;
- op_params.padding_values.height = data->padding.height;
- op_params.padding_values.width = data->padding.width;
- op_params.quantized_activation_min = data->output_activation_min;
- op_params.quantized_activation_max = data->output_activation_max;
+ op_params.padding_values.height = data.padding.height;
+ op_params.padding_values.width = data.padding.width;
+ op_params.quantized_activation_min = data.output_activation_min;
+ op_params.quantized_activation_max = data.output_activation_max;
reference_integer_ops::ConvPerChannel(
- op_params, data->per_channel_output_multiplier,
- data->per_channel_output_shift, GetTensorShape(input),
+ op_params, data.per_channel_output_multiplier,
+ data.per_channel_output_shift, GetTensorShape(input),
GetTensorData<int8>(input), GetTensorShape(filter),
GetTensorData<int8>(filter), GetTensorShape(bias),
GetTensorData<int32>(bias), GetTensorShape(output),
@@ -169,18 +230,18 @@ void EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node,
}
void EvalFloat(TfLiteContext* context, TfLiteNode* node,
- TfLiteConvParams* params, OpData* data,
+ TfLiteConvParams* params, const OpData& data,
const TfLiteTensor* input, const TfLiteTensor* filter,
const TfLiteTensor* bias, TfLiteTensor* im2col,
TfLiteTensor* hwcn_weights, TfLiteTensor* output) {
float output_activation_min, output_activation_max;
CalculateActivationRange(params->activation, &output_activation_min,
&output_activation_max);
-
+ // TODO(b/154032858): Investigate removing extra copies.
ConvParams op_params;
op_params.padding_type = RuntimePaddingType(params->padding);
- op_params.padding_values.width = data->padding.width;
- op_params.padding_values.height = data->padding.height;
+ op_params.padding_values.width = data.padding.width;
+ op_params.padding_values.height = data.padding.height;
op_params.stride_width = params->stride_width;
op_params.stride_height = params->stride_height;
op_params.dilation_width_factor = params->dilation_width_factor;
@@ -204,50 +265,20 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
const TfLiteTensor* bias = GetOptionalInputTensor(context, node, kBiasTensor);
- int input_width = input->dims->data[2];
- int input_height = input->dims->data[1];
- int filter_width = filter->dims->data[2];
- int filter_height = filter->dims->data[1];
- int output_width = output->dims->data[2];
- int output_height = output->dims->data[1];
-
- OpData data;
-
- // All per-channel quantized tensors need valid zero point and scale arrays.
- if (input->type == kTfLiteInt8) {
- TF_LITE_ENSURE_EQ(context, filter->quantization.type,
- kTfLiteAffineQuantization);
-
- const auto* affine_quantization =
- reinterpret_cast<TfLiteAffineQuantization*>(
- filter->quantization.params);
- TF_LITE_ENSURE(context, affine_quantization);
- TF_LITE_ENSURE(context, affine_quantization->scale);
- TF_LITE_ENSURE(context, affine_quantization->zero_point);
-
- TF_LITE_ENSURE(context,
- affine_quantization->scale->size == 1 ||
- affine_quantization->scale->size ==
- filter->dims->data[kConvQuantizedDimension]);
- TF_LITE_ENSURE_EQ(context, affine_quantization->scale->size,
- affine_quantization->zero_point->size);
- }
-
- TF_LITE_ENSURE_STATUS(CalculateOpData(
- context, node, params, input_width, input_height, filter_width,
- filter_height, output_width, output_height, input->type, &data));
+ TFLITE_DCHECK(node->user_data != nullptr);
+ const OpData& data = *(static_cast<const OpData*>(node->user_data));
switch (input->type) { // Already know in/out types are same.
case kTfLiteFloat32:
- EvalFloat(context, node, params, &data, input, filter, bias, nullptr,
+ EvalFloat(context, node, params, data, input, filter, bias, nullptr,
nullptr, output);
break;
case kTfLiteInt8:
- EvalQuantizedPerChannel(context, node, params, &data, input, filter, bias,
+ EvalQuantizedPerChannel(context, node, params, data, input, filter, bias,
output, nullptr);
break;
case kTfLiteUInt8:
- EvalQuantized(context, node, params, &data, input, filter, bias, nullptr,
+ EvalQuantized(context, node, params, data, input, filter, bias, nullptr,
nullptr, output);
break;
default:
@@ -261,9 +292,9 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
} // namespace conv
TfLiteRegistration* Register_CONV_2D() {
- static TfLiteRegistration r = {/*init=*/nullptr,
+ static TfLiteRegistration r = {/*init=*/conv::Init,
/*free=*/nullptr,
- /*prepare=*/nullptr,
+ /*prepare=*/conv::Prepare,
/*invoke=*/conv::Eval,
/*profiling_string=*/nullptr,
/*builtin_code=*/0,
diff --git a/tensorflow/lite/micro/kernels/xtensa_hifimini/conv.cc b/tensorflow/lite/micro/kernels/xtensa_hifimini/conv.cc
index 03eba5082af..8895ccf52d7 100644
--- a/tensorflow/lite/micro/kernels/xtensa_hifimini/conv.cc
+++ b/tensorflow/lite/micro/kernels/xtensa_hifimini/conv.cc
@@ -185,9 +185,6 @@ inline void Conv1x32Input32x32Filter(
ae_q56s acc_56 = AE_ZEROQ56();
const int8_t* input_vals_ptr = input_data - 2;
for (int i = 0; i < kFilterDepth; i += 2) {
- // Find current input index, minus 2 for Xtensa load
- // alignments:
-
// Load signed 2x 8bit values and right shift into 24bit
// alignment:
ae_p24x2s input_vals_24x2;
@@ -244,7 +241,6 @@ constexpr int kInputTensor = 0;
constexpr int kFilterTensor = 1;
constexpr int kBiasTensor = 2;
constexpr int kOutputTensor = 0;
-constexpr int kMaxChannels = 32;
// Conv is quantized along dimension 0:
// https://www.tensorflow.org/lite/performance/quantization_spec
@@ -258,9 +254,8 @@ struct OpData {
int output_shift;
// Per channel output multiplier and shift.
- // TODO(b/141139247): Allocate these dynamically when possible.
- int32_t per_channel_output_multiplier[kMaxChannels];
- int32_t per_channel_output_shift[kMaxChannels];
+ int32_t* per_channel_output_multiplier;
+ int32_t* per_channel_output_shift;
// The range of the fused activation layer. For example for kNone and
// uint8_t these would be 0 and 255.
@@ -268,12 +263,6 @@ struct OpData {
int32_t output_activation_max;
};
-// These constants represent constants specific to the music detect model.
-// They exist until (b/132070898) is fixed.
-static const int kMaxOpDataSize = 6;
-static int op_data_counter = 0;
-static OpData kStaticOpData[kMaxOpDataSize];
-
TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node,
TfLiteConvParams* params, int width, int height,
int filter_width, int filter_height, int out_width,
@@ -301,30 +290,37 @@ TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node,
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
int output_channels = filter->dims->data[kConvQuantizedDimension];
- TF_LITE_ENSURE_STATUS(tflite::PopulateConvolutionQuantizationParams(
+ return tflite::PopulateConvolutionQuantizationParams(
context, input, filter, bias, output, params->activation,
&data->output_multiplier, &data->output_shift,
&data->output_activation_min, &data->output_activation_max,
data->per_channel_output_multiplier,
reinterpret_cast<int*>(data->per_channel_output_shift),
- output_channels));
+ output_channels);
}
return kTfLiteOk;
}
-void Free(TfLiteContext* context, void* buffer) { op_data_counter = 0; }
+void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+ TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
+ void* data = nullptr;
+ if (context->AllocatePersistentBuffer(context, sizeof(OpData), &data) ==
+ kTfLiteError) {
+ return nullptr;
+ }
+ return data;
+}
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
+ TFLITE_DCHECK(node->user_data != nullptr);
+ TFLITE_DCHECK(node->builtin_data != nullptr);
auto* params = reinterpret_cast<TfLiteConvParams*>(node->builtin_data);
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
const TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
- // TODO(b/132070898): Use statically slotted OpData structures until a
- // scratch memory API is ready.
- OpData* op_data = &kStaticOpData[op_data_counter++];
- node->user_data = op_data;
+ auto* op_data = reinterpret_cast<OpData*>(node->user_data);
int input_width = input->dims->data[2];
int input_height = input->dims->data[1];
@@ -333,6 +329,17 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
int output_width = output->dims->data[2];
int output_height = output->dims->data[1];
+ // Per channel quantization is only needed for int8 inference. For other
+ // quantized types, only a single scale and zero point is needed.
+ const int num_channels = filter->dims->data[kConvQuantizedDimension];
+ // Dynimically allocate per-channel quantization parameters.
+ TF_LITE_ENSURE_STATUS(context->AllocatePersistentBuffer(
+ context, num_channels * sizeof(int32_t),
+ reinterpret_cast<void**>(&op_data->per_channel_output_multiplier)));
+ TF_LITE_ENSURE_STATUS(context->AllocatePersistentBuffer(
+ context, num_channels * sizeof(int32_t),
+ reinterpret_cast<void**>(&op_data->per_channel_output_shift)));
+
// All per-channel quantized tensors need valid zero point and scale arrays.
if (input->type == kTfLiteInt8) {
TF_LITE_ENSURE_EQ(context, filter->quantization.type,
@@ -353,11 +360,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
affine_quantization->zero_point->size);
}
- TF_LITE_ENSURE_STATUS(CalculateOpData(
- context, node, params, input_width, input_height, filter_width,
- filter_height, output_width, output_height, input->type, op_data));
-
- return kTfLiteOk;
+ return CalculateOpData(context, node, params, input_width, input_height,
+ filter_width, filter_height, output_width,
+ output_height, input->type, op_data);
}
void EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node,
@@ -366,6 +371,7 @@ void EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node,
const TfLiteTensor* filter,
const TfLiteTensor* bias, TfLiteTensor* output,
TfLiteTensor* im2col) {
+ // TODO(b/154032858): Investigate removing extra copies.
ConvParams op_params;
op_params.input_offset = -input->params.zero_point;
op_params.output_offset = output->params.zero_point;
@@ -388,6 +394,8 @@ void EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node,
}
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
+ TFLITE_DCHECK(node->user_data != nullptr);
+ TFLITE_DCHECK(node->builtin_data != nullptr);
auto* params = reinterpret_cast<TfLiteConvParams*>(node->builtin_data);
auto* op_data = reinterpret_cast<OpData*>(node->user_data);
@@ -429,8 +437,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
} // namespace conv
TfLiteRegistration* Register_CONV_2D() {
- static TfLiteRegistration r = {/*init=*/nullptr,
- /*free=*/conv::Free,
+ static TfLiteRegistration r = {/*init=*/conv::Init,
+ /*free=*/nullptr,
/*prepare=*/conv::Prepare,
/*invoke=*/conv::Eval,
/*profiling_string=*/nullptr,